import spaces
import torch
import re
import gradio as gr
from threading import Thread
from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
import subprocess

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

model_id = "vikhyatk/moondream2"
revision = "2024-05-20"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
moondream = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    revision=revision,
    torch_dtype=torch.bfloat16,
    device_map={"": "cuda"},
    attn_implementation="flash_attention_2"
)
moondream.eval()

@spaces.GPU(duration=10)
def answer_question(img, prompt):
    if img is None:
        raise gr.Error("Please upload an image.")

    image_embeds = moondream.encode_image(img)
    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
    thread = Thread(
        target=moondream.answer_question,
        kwargs={
            "image_embeds": image_embeds,
            "question": prompt,
            "tokenizer": tokenizer,
            "streamer": streamer,
        },
    )
    thread.start()
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer.strip()

with gr.Blocks() as demo:
    gr.Markdown(
        """
        # myAI - AMI Vision Module
        A lightweight Computer Vision model by @vikhyat - 🌔 [moondream2](https://github.com/vikhyat/moondream)
        """
    )
    with gr.Row():
        prompt = gr.Textbox(label="Input", value="Identify people in this image", scale=4)
        submit = gr.Button("Submit")
    with gr.Row():
        img = gr.Image(type="pil", label="Upload an Image")
        output = gr.TextArea(label="Response")

    submit.click(answer_question, [img, prompt], output)
    prompt.submit(answer_question, [img, prompt], output)

demo.queue().launch()